Fueling Your Business on Real-Time Analytics

Eric Frenkiel, MemSQL CEO

March 30, 2015 • Las Vegas, NV

From Spark to Ignition:

What’s in Store For This Presentation?

1. MemSQL:

A real-time database for transactions and analytics

2. Spark Use Cases

3. Example: Geospatial Enhancements

The real-time database for transactions and analytics

MemSQL Story

MemSQL Snapshot

Experienced Leadership

• Microsoft, Facebook, Oracle, Fusion-io

Inspired by Enterprise architecture gap

A real-time database for transactionsand analytics

• In-memory, distributed, SQL

Broad customer adoption across verticals

Top tier investors

Four Ways Your DBMS is Holding You Back

ETL (Extract, Transform, Load)

Analytic Latency

Synchronization

Copies of data

Source: Gartner Hybrid/Transactional/Analytical Processing Will Foster Opportunities for Dramatic Business Innovation

The Real-Time Database for Transactions and Analytics

Highest Value

Hot DataHigh Value

Cold Data

Analytics

Tra

nsa

ction

s

Data Loading and Queries

Aggregator Nodes

AvailabilityGroup 1

AvailabilityGroup 2

Cluster

Gartner Identifies Emerging Category:

HTAP (Hybrid Transactional/Analytical Processing)

“HTAP will enable business

leaders to perform…much

more advanced and

sophisticated real-time

analysis of their business

data than with traditional

architectures.”

Download at: memsql.com/gartner

Simple

Standard SQL

Transactions and analytics in one database

Behind the firewall or on the cloud

Flexible integrations (Hadoop, Spark, SQL)

Fast

Extremely low-latency queries

Massive parallel transaction capacity

Lock-free, shared-nothing architecture

Scalable

Scales out on cloud and commodity hardware

Deploys to thousands of machines

True linear scaling

Spark Use Cases

Spark Data Processing Framework

Intuitive, concise, and expressive operations needed for analytics

Spark

SQL

Spark

Streaming

Mllib

(machine

learning)

GraphX

(graph)

Apache Spark

Cluster-wide Parallelization | Bi-Directional

Understanding MemSQL and Spark

MemSQL and Spark Use Cases

Operationalize models built in Spark

Stream and event processing

Live dashboards and automated reports

Extend MemSQL analytics

Operationalize Models Built in Spark

Process in Spark, persist to MemSQL

Go to production and iterate faster

Enterprise

ConsumptionData into Spark

Model Creation Model Persistence

Stream and Event Processing

Structure event data on the fly

Pass to MemSQL for persistent, queryable format

Enterprise

Consumption

Real-time

Streaming Data

Data TransformationPersistent,

Queryable Format

Enterprise

Consumption

Events

KafkaApp

Singer Secor

Spark, MemSQL, Application

Real-Time Analytics at Pinterest

Higher performance event logging

Reliable log transport and storage

Faster query execution on real-time data

Extend MemSQL Analytics

The freshest data for analysis in Spark

Load from MemSQL to Spark and write results on return

Access to Live

Production DataReal-time Replica

Applications,

Data Streams

Interactive

Analytics,

Machine Learning

Live Dashboards and Automated Reports

Serve live dashboards from MemSQL

Run custom reports on live data with Spark

Live

Dashboards

Custom

Reporting

Access to Live

Production Data

SQL Transactions

and Analytics

Geospatial Enhancements

Geospatial Challenge

Commercial applications now geo-enabled

Location is everywhere

Lots of insight possible

Traditionally geo is processed separately

Real need for integrated geospatial at scale

MemSQL Geospatial

Points, Lines, and Polygons

Topological filters

Measurement functions

MemSQL Geospatial

BILLIONS of objects

Sub-second latency

Geo data is first-class citizen

Geo + Simplicity + Speed + Scale

Real-Time Geospatial Location Intelligence

Sample from 170 million taxi trips

Real-time ingest

Concurrent queries in fractions of a second

Unlimited number of geographic views

Simple queries while simultaneously ingesting data

Thank You!

Visit the MemSQL Booth #119

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